28 citations · 77 across the 25 of their papers we have counts for
13 papers · 1 filter
Nonconvex High-Dimensional Time-Varying Coefficient Estimation for Noisy High-Frequency Observations with a Factor Structure
Minseok Shin, Donggyu Kim
In this paper, we propose a novel high-dimensional time-varying coefficient estimator for noisy high-frequency observations with a factor structure. In high-frequency finance, we o…
Dynamic Realized Minimum Variance Portfolio Models
Donggyu Kim, Minseog Oh
This paper introduces a dynamic minimum variance portfolio (MVP) model using nonlinear volatility dynamic models, based on high-frequency financial data. Specifically, we impose an…
Robust High-Dimensional Time-Varying Coefficient Estimation
Minseok Shin, Donggyu Kim
In this paper, we develop a novel high-dimensional coefficient estimation procedure based on high-frequency data. Unlike usual high-dimensional regression procedures such as LASSO,…
Robust Realized Integrated Beta Estimator with Application to Dynamic Analysis of Integrated Beta
Minseog Oh, Donggyu Kim, Yazhen Wang
In this paper, we develop a robust non-parametric realized integrated beta estimator using high-frequency financial data contaminated by microstructure noises, which is robust to t…
Volatility Models for Stylized Facts of High-Frequency Financial Data
Donggyu Kim, Minseok Shin
This paper introduces novel volatility diffusion models to account for the stylized facts of high-frequency financial data such as volatility clustering, intra-day U-shape, and lev…
Dynamic Realized Beta Models Using Robust Realized Integrated Beta Estimator
Donggyu Kim, Minseog Oh, Minjeong Song +1
This paper introduces a unified parametric modeling approach for time-varying market betas that can accommodate continuous-time diffusion and discrete-time series models based on a…
Factor Overnight GARCH-Itô Models
Donggyu Kim, Minseog Oh, Xinyu Song +1
This paper introduces a unified factor overnight GARCH-Itô model for large volatility matrix estimation and prediction. To account for whole-day market dynamics, the proposed model…
Factor and Idiosyncratic VAR Volatility Matrix Models for Heavy-Tailed High-Frequency Financial Observations
Minseok Shin, Donggyu Kim, Yazhen Wang +1
This paper introduces a novel process for both factor and idiosyncratic volatility matrices whose eigenvalues follow the vector auto-regressive (VAR) model. We call it the factor a…
Conditional Quantile Analysis for Realized GARCH Models
Donggyu Kim, Minseog Oh, Yazhen Wang
This paper introduces a novel quantile approach to harness the high-frequency information and improve the daily conditional quantile estimation. Specifically, we model the conditio…
Unified Discrete-Time Factor Stochastic Volatility and Continuous-Time Ito Models for Combining Inference Based on Low-Frequency and High-Frequency
Donggyu Kim, Xinyu Song, Yazhen Wang
This paper introduces unified models for high-dimensional factor-based Ito process, which can accommodate both continuous-time Ito diffusion and discrete-time stochastic volatility…
Volatility Analysis with Realized GARCH-Ito Models
Xinyu Song, Donggyu Kim, Huiling Yuan +4
This paper introduces a unified approach for modeling high-frequency financial data that can accommodate both the continuous-time jump-diffusion and discrete-time realized GARCH mo…
Intelligent Initialization and Adaptive Thresholding for Iterative Matrix Completion; Some Statistical and Algorithmic Theory for Adaptive-Impute
Juhee Cho, Donggyu Kim, Karl Rohe
Over the past decade, various matrix completion algorithms have been developed. Thresholded singular value decomposition (SVD) is a popular technique in implementing many of them.…
Asymptotic Theory for Estimating the Singular Vectors and Values of a Partially-observed Low Rank Matrix with Noise
Juhee Cho, Donggyu Kim, Karl Rohe
Matrix completion algorithms recover a low rank matrix from a small fraction of the entries, each entry contaminated with additive errors. In practice, the singular vectors and sin…